Predictive monitoring for vehicle efficiency and maintenance

ABSTRACT

A system and method for monitoring fuel efficiency of a vehicle is provided. Fuel efficiency of a vehicle may be monitored in real-time using telemetry transmitted from the vehicle. The telemetry may be analyzed in combination with a fuel efficiency model in order to determine whether the real-time fuel efficiency determination deviates from a baseline fuel efficiency. The real-time fuel efficiency monitoring system and method may be used for feedback for the driver, such as sending real-time messages to the driver to modify operation of the vehicle to increase fuel efficiency. The fuel efficiency model may further be used to assign vehicles in a fleet to particular routes, vehicles to particular drivers, or vehicles to particular routes with particular drivers.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.60/699,585, filed Jul. 14, 2005. The entirety of U.S. ProvisionalApplication No. 60/699,585 is hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention relates to systems for monitoring efficiency andpredicting failures for mechanical devices, such as vehicles. Inparticular, the invention relates to systems capable of real-timemonitoring of fuel efficiency and predictive maintenance for vehicles.

2. Related Art

Demand for energy, such as oil, gas, electricity, is at an all-time highand is predicted to increase for the foreseeable future. This increasingdemand is in contrast to the resources for energy that remainessentially static. Because of this, energy prices will likely continueto rise.

Faced with these rising energy costs, many companies have attempted toreduce energy consumption for their vehicles. One way is for the companyto collect and analyze past energy usage for the vehicles. For example,the amount of fuel used and the number of miles driven over one or moremonths are typically collected. The collected energy data is thenanalyzed in order to determine the fuel efficiency of the vehicles, andto compare the determined fuel efficiency with an expected fuelefficiency. If the determined fuel efficiency is lower that expected,this may indicate a problem in the vehicle, such as requiringmaintenance of the vehicle. The vehicle may then be serviced in order tocorrect for the lower than expected fuel efficiency.

While this method may improve fuel efficiency of vehicles, given theincreasing cost of energy, there is a need to further improve fuelefficiency of vehicles.

SUMMARY

In one embodiment, a real-time energy efficiency determination systemand method are provided. Energy efficiency may relate to conserving anytype of energy (such as gasoline, electricity, or the like) for any typeof mechanical device, such as a vehicle (e.g., bus, car, train,airplane, boat), or a manufacturing machine (e.g., an assembly machine,robot, or the like). One or more operational characteristics of themechanical device, such as the vehicle's average fuel economy, may betransmitted to a central system. The central system may analyze thetransmitted operational characteristic along with an efficiency modelthat may include an optimum fuel efficiency for the vehicle. Forexample, the central system may analyze average fuel efficiency varianceby subtracting an average fuel efficiency transmitted from the vehiclefrom the optimum fuel efficiency in the efficiency model. The centralsystem may then assess whether to modify operation of the vehicle toimprove fuel efficiency (such as whether to repair the vehicle toimprove fuel efficiency).

The efficiency determination system may work in combination with afailure prediction system. The failure prediction system may use anoperational model to predict future operation of the vehicle, such aswhether there may be an equipment failure in the vehicle. The centralsystem may assess the impact of the predicted failure and the impact ofthe potential remedies to determine whether to repair the vehicle.

In another embodiment, a real-time energy efficiency determinationsystem and method are provided for feedback of the operation of avehicle. Operational data may be sent via telemetry to the centralstation. The central system may analyze the transmitted operationalcharacteristic along with an efficiency model to analyze the operationof the vehicle in real-time. The efficiency model may include optimumoperating conditions for fuel efficiency for the vehicle (e.g., rate ofacceleration, rate of breaking, speed fluctuation, and speed duringcornering). The central system may then assess a driver of the vehiclebased on the real-time operational characteristics of the vehicle andthe vehicle efficiency model, and generate a recommendation as to theassessment of the driver of the vehicle (such as transmitting to thedriver in real time a recommendation to modify driving to improve fuelefficiency, or such as generating a report regarding the assessment ofthe driver of the vehicle).

In still another embodiment, a vehicle allocation system and method isprovided. Each vehicle in a fleet may be modeled for fuel efficiency forone or more routes (or one or more characteristics of routes), and/orone or more drivers. The central system may access the model for eachvehicle in order to assign a particular vehicle to a particular route,assign a particular driver to driver a particular vehicle, and/or assigna particular driver to drive a particular route using a particularvehicle. This driver and/or route optimization process may be incommunication with the enterprise scheduling systems to check foravailability and automatically assign drivers and vehicles to theappropriate routes. In this manner, the central system may improveoverall fleet efficiency by creating vehicle-route, vehicle-driver, orvehicle-route-driver match-ups that provide the optimal result.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be better understood with reference to the followingdrawings and description. The components in the figures are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention. Moreover, in the figures, likereferenced numerals designate corresponding parts throughout thedifferent views.

FIG. 1 is a block diagram of one configuration of the invention of thefuel efficiency system including vehicles, a wireless communicationdevice and a central system.

FIG. 2 is an expanded block diagram of the central system shown in FIG.1.

FIG. 3 is one example of a flow chart of operation of the fuelefficiency system.

FIG. 4 is an example of a flow chart for analyzing the real-time fuelefficiency of the vehicle.

FIG. 5 is an example of a flow chart for predicting failures of thevehicle.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Public or private organizations typically have assets used to operatethe organizations. The assets may include one or more types ofmechanical equipment, such as a vehicle (e.g., car, bus, train,airplane, or the like) or a manufacturing machine (e.g., an assemblymachine, robot, or the like). These assets typically operate using oneor more types of energy. For example, a vehicle may operate usinggasoline, diesel fuel, etc. The organization may wish to reduce theamount of energy used to operate the mechanical equipment, such asreduce the amount of gasoline or diesel fuel used to operate anorganization's fleet of vehicles.

One manner in which to reduce the amount of energy used is to model theorganization's mechanical equipment. For example, each vehicle within anorganization's fleet may be modeled. And, any aspect of the vehicleswithin an organization may be modeled. As discussed in more detailbelow, the model may comprise subparts that relate to different aspectsof the vehicle. One part of the model may comprise an optimum fuelefficiency for the vehicle. Another part of the model may compriseoptimum operating conditions for the vehicle (e.g., rate ofacceleration, rate of breaking, speed fluctuation, and speed duringcornering). The model, in combination with real-time data generated fromthe vehicle, may be used: (1) to determine real-time fuel efficiency ofthe vehicle; (2) to determine whether to modify operation of the vehicleto improve fuel efficiency (such as whether to repair the vehicle toimprove fuel efficiency); (3) to predict future operation of the vehicle(such as whether there may be an equipment failure in the vehicle); (4)to analyze the driving patterns of the driver (and potentially providefeedback to the driver); (5) to determine how to allocate vehicles in afleet (e.g., a vehicle fuel efficiency model that models the fuelefficiency based on one of the following variables, driver, route,characteristics of the road, weather, condition of road, time of day,etc. may be used to determine which vehicle should be assigned to whichdriver, which route, which weather, which road, which time of day,etc.); and (6) to analyze routes and efficiency to determine whichroutes lead to lower efficiency and why, and to recommend solutions tocorrect for lower efficiency of certain routes.

The models for the vehicles may be used in combination with datagenerated onboard the vehicle, passive data synchronization (i.e.,transmission of the data from the vehicle to a central system withoutthe interaction of the operator of the vehicle), and automated provisionof warnings of decreased energy efficiency. More or fewer components maybe used. Real-time data may be generated on the vehicle of variousaspects of the current state of the vehicle (such as data indicative ofcurrent fuel efficiency, current operation, current position, etc.).Further, the real-time data may be generated at any interval, such as atcontinuous intervals in an “always-on” manner. The real-time data may betransmitted to the central system in the form of telemetry data feeds.The central system may analyze the real-time data in combination withthe model, and determine the state of the vehicle (such as determinecurrent real-time energy efficiency operation and/or predict futureoperation of the vehicle, such as a breakdown in the vehicle).Maintenance organizations may then use these efficiency predictions toprioritize and optimize provision of preventative maintenance.

Thus, the central system may analyze one or both of: (1) the real-timeefficiency of the vehicle; and (2) predict conditions that may affectthe efficiency of the vehicle and prevent the conditions from occurringbefore excess fuel or energy is consumed. The analysis of the centralsystem is different from previous energy analysis systems in severalrespects. First, the real-time analysis is different from previouspassive, after-the-fact analysis performed on historical data at monthlyintervals. Second, the use of the model is prospective, looking to thefuture of the operation of the vehicle. This is in contrast to previousefficiency systems that are retrospective, reacting to low efficiency ofthe vehicle after the fact.

Turning to the drawings, FIG. 1 shows a block diagram of oneconfiguration of the fuel efficiency system 100. The fuel efficiencysystem 100 may include a central system 110 that communicates with oneor more vehicles (vehicle A 130, vehicle B 130, . . . vehicle Z 130)wirelessly through wireless communication device 120. The wirelesscommunication may be one or more of the following: satellite, cellular,or private radio frequency (RF). Other types of wireless communicationmay be used. Though FIG. 1 depicts a wireless system, communicationbetween the monitored mechanical equipment may be wired, or acombination of wired and wireless. For example, if the mechanicalequipment to be monitored is a machine on a manufacturing assembly line,the communication to the central system 110 may be wired, obviating theneed for wireless communication device 120. Moreover, the vehicle mayinclude one or more sensors, as discussed in more detail below, thatgenerate data indicative of the current state of the vehicle (such ascurrent mechanical or electrical operation, current position, etc.).

FIG. 2 illustrates an expanded block diagram of the central system 110depicted in FIG. 1. Central system 110 may comprise a general purposecomputing device, including a processing unit 228, a system memory 220,and a system bus 234, that couples various system components includingthe system memory 220 to the processing unit 228. The processing unit228 may perform arithmetic, logic and/or control operations by accessingsystem memory 220. The system memory 220 may store information and/orinstructions for use in combination with processing unit 228. The systemmemory 220 may include volatile and non-volatile memory, such as randomaccess memory (RAM) and read only memory (ROM). RAM may includecomputer-readable programming corresponding to the flow charts in FIGS.3-5 and may include one or more software programs, such as fuelefficiency module 222 (which may access the fuel efficiency model),predictive failure module 224 (which may access the operational model),assess module 225, alert module 226, and action module 227. The systembus 234 may be any of several types of bus structures including a memorybus or memory controller, a peripheral bus, and a local bus using any ofa variety of bus architectures.

Central system 110 may receive input from the vehicles 130 via wirelesscommunication interface 120. Central system 110 may further include adata storage interface 236 for reading from and writing to a datastorage device 238, and an external memory interface 240 for readingfrom or writing to an external database 242. The data storage device 238may include a hard disk, flash-type memory, or other non-volatilememory. The external database 242 may store the models for the vehicles.Although the exemplary environment described herein employs a hard disk238 and an external database 242, other types of computer readable mediawhich can store data that is accessible by a computer, such as magneticcassettes, flash memory cards, random access memories, read onlymemories, and the like, may also be used in the exemplary operatingenvironment. Though FIG. 2 depicts system memory 220 as storing fuelefficiency module 222, predictive failure module 224, assess module 225,alert module 226, and action module 227, any one of, any combination of,or all of the hard disk 238, external database 242, or system memory 222may store the modules.

The fuel efficiency system 100 may operate in various ways. One exampleis a seven-step process that includes: (1) collecting data; (2)transmitting the data; (3) receiving (and optionally validating thedata); (4) storing (and optionally processing and cleaning) the data;(5) analyzing and/or predicting; (6) assessing and alerting; and (7)taking action. More or fewer steps may be performed. FIG. 3 illustratesone example of a flow chart 300 of the various steps listed above. Asshown at block 302, the data is collected. For example, various sensorsmay be used on a vehicle to collect data regarding characteristics ofthe vehicle. As shown at block 304, the data may be transmitted to thecentral system. Data may be transmitted in real-time, such asapproximately when the data is generated. Or the data may be transmittedin batches, with the data being stored for a predetermined amount oftime in the vehicle prior to transmission. As shown at block 306, thedata may be received. In addition to receiving the data, the data mayalso be validated. Validation may comprise analysis of the data todetermine whether the data should be processed or should be rejected dueto errors, such as sensor malfunctions or errors in transmission. Asshown at block 308, the data is stored. In addition, the stored data mayoptionally be processed and cleaned.

The data may be analyzed in order to determine the real-time fuelefficiency of the vehicle, as shown at block 310. For example, the fuelefficiency module 222 may perform a statistical analysis in real-timeusing the fuel efficiency model to determine the presence of anysignificant fluctuations. Further, the statistical analysis may beperformed continuously in an “always on” manner. In one embodiment,every piece of telemetry received from the vehicle may be collected andstatistically analyzed, rather than solely sampling efficiency atintervals. As discussed in more detail below, the analysis may includeefficiency regarding road miles covered throughout a duty cycle per unitof fuel consumed. The efficiency may be compared with a part or all ofthe fuel efficiency model of the vehicle, such as a baseline fuelefficiency. For example, an ongoing measurement of current efficiencymay be compared to the baseline efficiency in the model, and degradedperformance may be detected.

The data may also be used to predict failures in the vehicle, as shownat block 312. For example, the predict failure module 224 may comparethe data with the operational model for the vehicle to predict potentialfailures. An assessment is made of the impact of the real-time fuelefficiency and/or the predicted failures, as shown at block 314. Basedon the assessment, a determination is made (block 316) whether to sendan alert (block 318) and/or perform an action (block 320).

The fuel efficiency model may provide one or more outputs. One exampleof an output of the fuel efficiency model may comprise the optimum fuelefficiency of the vehicle (such as the optimum fuel rate). Anotherexample of an output may comprise the conditions that may supportoptimum fuel efficiency. For example, the rate of acceleration, rate ofbraking, speed fluctuation, speed during cornering, etc. may compriseconditions that support optimum fuel efficiency. These conditions areprovided merely for illustrative purposes. Other conditions may be used.

The fuel efficiency model may include zero, one, or more inputsdepending on the complexity of the model. For example, at its simplest,the fuel efficiency model may include no inputs, merely requesting theoptimum fuel efficiency of the vehicle. Specifically, at its simplest,the fuel efficiency model may comprise a baseline or optimum fuelefficiency for the vehicle (such as a single number that indicates thefuel volume consumed per unit of distance traveled (e.g., miles pergallon)). At its more complex, the fuel efficiency model may depend oncertain variables and input one or more variables, such as: (1) thecharacteristics of the road (e.g., type of road (e.g., highway versuscity), altitude changes (e.g., number of hills); speed limits; conditionof road (poor or good)); (2) weather (rain, snow, sunny); and (3)operational characteristics of the vehicle (e.g., load of vehicle (e.g.,fully loaded with passengers), certain mechanical systems operating(air-conditioning operating or not operating)). Thus, the fuelefficiency model at its more complex may have one or more optimum fuelefficiencies or optimum conditions depending on the variable orvariables input to the model (e.g., one optimum fuel efficiency forhighway operation with the air conditioning not operating, and anotheroptimum fuel efficiency for city operation with the air conditioningoperating; or one optimum rate of acceleration during rain and anotheroptimum rate of acceleration during snow). At its even more complex, thefuel efficiency model may include additional variables tailored to thespecific application of the vehicle, such as specific driver operatingvehicle (e.g., running average or optimum fuel efficiency for differentdrivers that operate the vehicle), specific route (e.g., #157 bus routeversus #151 bus route). Again, the fuel efficiency model at its morecomplex may have one or more optimum fuel efficiencies or optimumconditions depending on the variable or variables input to the model(e.g., one fuel efficiency for driver #1 driving #157 bus route, andanother fuel efficiency for driver #2 driving #151 bus route). Theseinput variables for the fuel efficiency model are provided merely forillustrative purposes. Other variables may be used.

The fuel efficiency model may be generated in a variety of ways, such asby performing an initial empirical study of fuel added and road milescompleted before exhaustion, or from a passive study of existing data.The empirical study or passive study may be performed on the specificvehicle for which the model is created or for a vehicle with the samemake, model, options, etc. The fuel efficiency model may also begenerated by obtaining telemetry data (such as data for specific driverson specific routes)

The fuel efficiency model may be used to determine fuel efficiency ofthe vehicle in real-time. As discussed in block 310 above, a vehicle'sfuel efficiency (e.g., amount of gasoline used) may be analyzed. Thefuel efficiency of the vehicle may be analyzed in a variety of ways, andmay depend on the vehicles and engines varying levels of sophisticationand varying amounts of operational data reported. For example, asdiscussed below, there may be three different types of fuel efficiencyanalysis including: (1) Average Fuel Efficiency Variance Analysis; (2)Implied Average Fuel Efficiency Variance Analysis; and (3) ExtrapolatedAverage Fuel Efficiency Variance Analysis. Other types of statisticalanalysis may be performed. Further, these analyses may vary fordifferent engine types (e.g. diesel, compressed natural gas, hybrid,etc.).

For other mechanical devices, such as an assembly machine, robot, etc.the amount of electricity or other type of energy may be analyzed. Priorto the real-time statistical analysis of the energy efficiency of themechanical device, the fuel efficiency model may be generated.

Referring to FIG. 4, there is shown a flow chart of block 310 depictedin FIG. 3. As shown at block 402, a real-time fuel efficiency criterionmay be obtained from the vehicle. The real-time fuel efficiencycriterion may be one of several indicators of the vehicle's fueleconomy, as discussed below. An indicator of the real-time fuelefficiency may be determined based on the real-time fuel efficiencycriterion, as shown at block 404. For example, the real-timefuel-efficiency of the vehicle may be determined by only examining thereal-time fuel efficiency criterion (e.g., current fuel consumption ofthe vehicle, current fuel rate, etc.). Or, the fuel efficiency of thevehicle may be based on a combination, such as an average, of thereal-time fuel efficiency criterion and previous determinations of fuelefficiency. Averaging may increase the reliability of the determinedfuel efficiency. After the indicator of the real-time fuel efficiency isdetermined, the baseline fuel efficiency is accessed (block 406) and thetwo are compared (block 408). As discussed below, the comparison may beperformed in a variety of ways, such as by generating an average fuelefficiency variance analysis, implied average fuel efficiency varianceanalysis, and extrapolated average fuel efficiency variance analysis.The comparison may be analyzed in the assess module 225, alert module226, and/or the action module 227. Further, the historical database(such as database 242) is updated with the data, as shown at block 412.

One example of a statistical analysis of a fuel efficiency criterion isthe vehicle's average fuel economy. The vehicle itself may calculate itsaverage fuel economy over a preconfigured distance. Several automotivestandards specify certain values to generate in controlling a vehicle.One standard is the Society of Automotive Engineers (SAE) J1708 standardfor vehicle communications, which specifies that in the stream ofvalues, PID 185 represents the vehicle's average fuel economy. Asdiscussed above at block 404, an indicator of real-time fuel efficiencyis determined based on the real-time fuel efficiency criterion. Thisstep may not be necessary, such as in the case where the vehicletransmits its average fuel efficiency. Thus, this value may be used astransmitted, or may be combined with previous reading's of average fuelefficiency, such as further averaged over a vehicle's duty cycle toincrease reliability (e.g., over a 7 day period for a city bus to ensurethat all passenger load configurations are included in the sample). Thefuel efficiency (either the transmitted or average fuel efficiency) maybe subtracted from the baseline fuel efficiency to yield the variance infuel efficiency over time. The time period may be a configurableinterval. For example, the time period may be since the last time theoperation was performed. This value of the average fuel efficiencyvariance may be output to the assess module 225, alert module 226,and/or the action module 227.

Another example of a statistical analysis of a fuel efficiency criterionis the vehicle's total fuel consumed. In the SAE J1708 standard, PID 250represents the vehicle's total fuel consumed. Subtracting this valuefrom the previous value retrieved yields the fuel consumption during apredetermined period. This value may be used as-is or may be furtheraveraged over a vehicle's duty cycle to increase reliability (e.g., overa 7 day period for a city bus to ensure that all passenger loadconfigurations are included in the sample). The actual road milestraveled by the vehicle may be determined from values received from aGlobal Positioning System (GPS) receiver on the vehicle. Dividing theactual road miles by the per unit fuel consumption determined aboveyields an objective real-time measure of fuel efficiency. Subtractingthe real-time fuel efficiency determined from the baseline fuelefficiency yields the variance in fuel efficiency over time (e.g., thetime period being since the last time the operation was performed, whichis a configurable interval in the system). This value of the impliedaverage fuel efficiency variance may be output to the assess module 225,the alert module 226, and/or the action module 227.

Still another example of a statistical analysis of a fuel efficiencycriterion is the vehicle's fuel rate. In the SAE J1708 standard, PID 183represents the vehicle's fuel rate. Averaging this value over timeyields the long term fuel rate consumption. This value may be used as-isor may be further averaged over a vehicle's duty cycle to increasereliability (e.g., over a 7 day period for a city bus to ensure that allpassenger load configurations are included in the sample). Multiplyingthe value by the time interval of data collection yields the fuelconsumption (e.g., fuel rate of 0.5 liters per minute times 20 minutesgives us 10 liters of fuel consumed). Previous steps of data acquisitiondeliver a stream of values of the actual road miles traveled by thevehicle. Dividing this data by the per unit fuel consumption yields anobjective measure of fuel efficiency. Subtracting the fuel efficiencyfrom the baseline fuel efficiency yields the variance in fuel efficiencyover time (e.g., the time period being since the last time the operationwas performed, which is a configurable interval in the system). Thisvalue of the extrapolated average fuel efficiency variance may be outputto the assess module 225, the alert module 226, and/or the action module227.

Predicting failures, discussed above in block 312, may determine ifthere are any equipment failures in early stages in the asset, and ifso, how and when the failures will occur. In the example of vehicles,equipment failures may include items like piston cracks, fouledturbochargers, oil leakage, etc. These examples of equipment failuresare provided for illustrative purposes. Other failures may be predictedas well. One example of a system for predicting failures is bySmartSignal Corporation of Lisle, Ill. SmartSignal Corporation offersEPI*Center software solution that continuously inspects equipmentperformance to identify performance failures before they happen. As aresult, corrective action may be taken at the lowest cost and with theleast disruption to operations. An example of a patent application bySmartSignal Corporation is entitled “Diagnostic Systems and Methods forPredictive Condition Monitoring,” U.S. application Ser. No. 10/681,888,filed on Oct. 9, 2003, Publication No. 2004/0078171 A1. U.S. applicationSer. No. 10/681,888, filed on Oct. 9, 2003, Publication No. 2004/0078171A1 is incorporated by reference herein. The fuel efficiency statisticalanalysis discussed in regard to block 310 may be performed inconjunction with the predictive failure analysis discussed in block 312.FIG. 5 illustrates a flow chart of block 312 depicted in FIG. 3.

As shown at block 502, the baseline model for the operational model ofthe vehicle is accessed. The operational model may be aggregated overtime from data delivered to the central system 110 via the wirelesscommunication described above, or gathered through other means. It mayinclude a period of continuous operational data of the vehicle, such astwo weeks of data, for various data points of the vehicle. The followingis an exemplary list of sixteen data points: (1) Road speed; (2) Enginespeed; (3) Engine load; (4) Fuel rate; (5) Accelerator pedal position;(6) Coolant temperature; (7) Instantaneous fuel economy; (8) Boostpressure; (9) Oil pressure; (10) Manifold pressure; (11) OilTemperature; (12) Fuel Consumption; (13) Transmission oil temperature;(14) Transmission range selected; (15) Transmission range attained; and(16) Hydraulic retarder oil temperature. Fewer, greater, or differentdata points may be used to generate the operational model.

The predict failure module may access the data points (block 504). Theoperational model may be divided into two or more sub-models comprisinga primary (engine) model and an auxiliary (transmission and cooling)model. A continuous data feed may be established to the operationalmodel from the vehicle. For example, data may flow from the sixteendimensions outlined above plus any additional sensor feeds to theoperational model by way of a flat file export from a database (e.g.,Microsoft's SQL Server DTS functionality) into a directory that isregularly polled.

The predict failure module 224 may use the operational model to analyzethe data points to predict failures, as shown at block 506.Specifically, the predict failure module 224 may perform a complexseries of vector ordering calculations on the input data and generate aset of residuals for each sensor, such as a measure of the variancebetween the expected sensor value given all other sensor values and theactual sensor value from the data stream. If the residual is greaterthan a preset threshold (that may be determined when the operationalmodel is created), an alert is generated (block 508).

When alerts are generated, both the urgency of the event and the type ofimpending failure may be estimated by the operational model, as shown atblock 510. The urgency may be determined by the number of alertsgenerated in a period of time and the relative size of the residual. Thetype of failure may be extrapolated from the particular sensor (orcombination of sensors) which are deviating from the expected (e.g.positive residuals on engine oil temperature and coolant temperature anda negative residual on coolant pressure would indicate a bad coolantpump). These two data points, predicted failure description and urgencyfactor, may be output from the predict failure module 224.

The outputs of block 310 (assessing fuel efficiency) and block 312(predicting failures) may be input to block 314 (assessing impact ofdecrease efficiency and/or failures). The assess module 225 may focus ondetermining the impact of failures and decreased efficiency onoperations from a holistic standpoint and the cost impacts of somepossible responses. For example, some failures or degraded efficiencymay have direct costs associated with them, while others may incurlatent fees that require additional calculations. Further, in somecases, the maintenance to be performed may be more costly than thebreakdown. As discussed in more detail below, this may be considered inassessing the impact of the failure or decreased efficiency. Theassessing process may additionally search for reasons for degradedefficiency unrelated to the mechanical condition of the vehicle, such asdriver behavior and route selection.

The degraded fuel efficiency may be analyzed by the assess module 225 inseveral ways. For example, the impact(s) of degraded efficiency and itspotential remedies (such as repairing the vehicle) may be calculated inthree steps. When the fuel efficiency module 222 detects degradedefficiency, the amount of the degradation may be sent to the assessmodule 225. The assess module may multiply the increased fuelconsumption by the amount of time until the next scheduled maintenanceprocedure (that would presumably alleviate the efficiency problem).Multiplying by the expected future cost of fuel yields the expectedincremental cost of the inefficiency.

The cost of early corrective maintenance may be calculated in a holisticmanner, such as by adding together one, some, or all of the followingcosts: (1) opportunity cost of removing the unit from service formaintenance (including lost fees or cartage revenue); (2) labor cost ofservicing the unit outside its existing schedule (labor cost may involvetechnician overtime); (3) parts purchases if necessary; and (4) use ofspare units during the maintenance to maintain service levels (and theirassociated depreciation, etc.). The costs are merely for illustrativepurposes. Other costs may be analyzed.

The cost impact of running at a decreased level of efficiency determinedabove and the cost impact of performing maintenance to correct thedecreased inefficiency may be compared, and the lesser of the two may bedetermined to be preferable. This problem, suggested resolution, andfinancial impacts may then be passed to the alert module 226.

Another example of the assess module 225 analyzing the degraded fuelefficiency may focus on non-mechanical (e.g., non-repair) causes.Specifically, the causes, impacts and potential remedies fornon-mechanical efficiency degradation may be analyzed. First, a datasource containing driver, vehicle and route information for each tripmay be merged with the real-time efficiency data from the fuelefficiency module. As discussed above, the fuel efficiency model for avehicle may include a variable based on the driver and routeinformation. The real-time efficiency data may be used to generate thatportion of the model. Second, driving patterns may be correlated toreal-time efficiency in order to determine driving patterns that areconducive to optimal efficiency. As discussed above, the fuel efficiencymodel may include optimal conditions. The conditions of driving patternsin question may include, but are not limited to: (1) rate ofacceleration; (2) rate of breaking; (3) speed fluctuation; and (4) speedduring cornering.

Third, correlation analysis may be performed between routes andefficiency to determine which routes lead to lower efficiency and why.The assessment may be taken extended by utilizing the geographical (forexample from GPS) and route data to perform correlation analysis ofefficiency versus route parameters such as, but not limited to: (1)altitude changes (e.g., hills); (2) number and angle of turns; (3)number and frequency of stops; (4) speed limit along the route segments;and (5) road type (highway versus city street versus rural road). Theassessment may then be used to propose modified routes, or modifieddriving along existing routes in order to increase fuel efficiency.

The assess module 225 may also assess the impact(s) of predicted failureand its potential remedies. This may be performed in four steps. First,when the predict failures module 224 predicts an equipment failure, thatprediction and the subsystem(s) affected may be sent to the assessmodule 225. The assess module may first examine the subsystem(s)affected and make a determination of urgency based on whether or notsystem safety is impacted (e.g., if vehicle braking is in danger ofbeing compromised).

Second, the cost of the outage, such as of letting the unit fail, may becalculated in a holistic manner, adding together one, some, or all ofthe following costs: (1) opportunity cost of removing the unit fromservice for maintenance (including lost fees or cartage revenue); (2)any penalties associated with removing the unit from service unscheduled(e.g., violations of service level agreements); (3) charges associatedwith expedited parts purchases if necessary; (4) use or rental of spareunits during the maintenance to maintain service levels (and theirassociated depreciation, etc.); (5) on-site towing, maintenance andsupport personnel, municipal fees, and associated overtime ifapplicable; (6) replacement of spoiled cargo; and (7) impact of cascadefailures in which one failure causes others (e.g. diesel turbochargerblades being thrown into intake valves).

Third, the cost of early corrective maintenance may also be calculatedin a holistic manner, adding together one, some, or all of the followingcosts: (1) opportunity cost of removing the unit from service formaintenance (including lost fees or cartage revenue); (2) labor cost ofservicing the unit outside its existing schedule (labor cost may involvetechnician overtime); (3) parts purchases if necessary; and (4) use ofspare units during the maintenance to maintain service levels (and theirassociated depreciation, etc.). The costs are merely for illustrativepurposes. Other costs may be analyzed.

Fourth, the cost impact of the outage determined above and the costimpact of performing maintenance to prevent the outage determined abovemay be compared, and the lesser of the two may be determined to bepreferable. The problem, suggested resolution, related financialimpacts, and the urgency assessment may be passed to the alert module226.

The alert module 226 may inform the appropriate people within theorganization of maintenance or efficiency problems, and the potentialoptions for resolution. The alert module 226 may provide alerts for thepurpose of affecting future behavior. For example, the alerting processmay include providing reports detailing and ranking driving behavior ofeach driver, including developmental points for each. As anotherexample, the alerting process may report similar ratings for routes, tohelp the appropriate people better understand which routes are causingthe overall efficiency to decrease, and what attributes (such asaltitude change, number and angle of turns, number and frequency ofstops, speed limit along route segments, and road type) of these routesmay be causing the decrease. A variety of factors are considered in theprocess in order to ensure an appropriate response is coordinated asexpediently as is appropriate. The alert module 226 may provide alertsfor the purpose of affecting current or real-time behavior. For example,the alert module 226 may provide real-time feedback to drivers to modifytheir behavior in a way that would have a positive effect on efficiency.As discussed below, driver alerts may be issued on an output device(such as a screen and/or a speaker) in the vehicle in real-time.

In the context of a maintenance event (such as either decreasedefficiency or a predicted failure), suggested resolution, financialimpact, and urgency factor may be received from the assess module 225. Aseries of logic steps and lookups may be performed to determine whichpersonnel to alert and through which channel.

The process may comprise four steps; however, fewer or greater steps maybe used. First, a determination may be made whom to alert. This decisionmay be based on a holistic assessment of a variety of factors to makesure an organizationally expedient route is taken to problem resolution.If the urgency indicator specifies that there may be a safety issueengendered in a failure, operations supervisors along with regulatorybodies (e.g., OSHA, labor union wardens, etc.) may be involved. Ifsafety is not threatened, lower level technicians may be moreappropriate. The type of maintenance required may be a concern as manyoperations departments are divided into functional areas (more so inunionized environments). Finally, the overall cost of the outage andresponse may be considered, and supervisors or department heads may beinvolved if a predetermined dollar threshold is exceeded.

Second, a determination may be made how to alert the appropriateparties. This decision may be based on the urgency of the alert (e.g.,page or telephone for higher priorities, email for lower), as well asthe preferences of the parties involved. Third, the alert may betransmitted. Fourth, any actions taken by the parties alerted (e.g.,acknowledgement and dismissal, forward to another party, or maintenanceperformed) may be logged, stored, and attributed in the system to theuser involved. A facility for auditing of response times and actionstaken may be included in the system for subsequent analysis.

In addition to alerting regarding a maintenance event, the alert module226 may generate driver and route impact reports at regular orpredetermined time intervals. These reports may be made available to theappropriate people within the organization for review and action, asdiscussed below.

Further, the alert module 226 may issue driver alerts. Driver alerts maybe sent from the central system 110 to the vehicles 130 to be displayedon an output device (such as a visual alert on a display and/or an audioalert on a speaker) on the vehicle. The alerts may include: (1)on-screen instructions on how to improve driving behavior pointing outspecific areas for improvement, such as slower cornering, earlierdeceleration, etc.; (2) voice alerts when the driver is doing somethingthat is known to cause lower efficiency (e.g., rapid acceleration); (3)current driving score (e.g., a score indicative of how fuel efficientthe driver is driving the vehicle); and (4) cumulative driving score forthe past week, month or other time period on which the drivers areevaluated. These alerts are provided merely for illustrative purposes.Other or different alerts may be issued.

The action module 227 may take one or more actions in response to theassess module 225 and/or alert module 226. For example, the actionmodule 227 may schedule work in order to repair the vehicle, may orderparts to repair the vehicle, and/or may dispatch workers or alternatevehicles in order to repair the vehicle.

The action module 227 may also assign vehicles to particular routesand/or particular drivers. Specifically, a driver and/or routeoptimization process may be performed by the action module 227 in orderto improve the overall fleet efficiency by creating vehicle-route,vehicle-driver, or vehicle-route-driver match-ups that provide theoptimal result. Specifically, the fuel efficiency model may include dataregarding the vehicle for a particular route and/or for a particulardriver. The action module may access this data in order to assign aparticular vehicle to a particular route, assign a particular driver todriver a particular vehicle, and/or assign a particular driver to drivea particular route using a particular vehicle. The driver and/or routeoptimization process may be in communication with the enterprisescheduling systems to check for availability and automatically assigndrivers and vehicles to the appropriate routes. The driver and/or routeoptimization process may have two main sources of input data: (1)information generated in the assess module 225 on vehicle efficiency asrelated to route and/or driver (e.g., how a particular vehicle performson a particular route with a particular driver); and (2) data in theenterprise scheduling system to provide information on availablevehicles, available drivers, length of time to complete a route andother necessary inputs. The action module 227 may then run optimizationalgorithms to determine best possible match-ups of vehicles to driversto routes, to achieve maximum efficiency.

Alternatively, the fuel efficiency model may include data regarding thevehicle for characteristics of a route (such as fuel efficiency forhighway versus city, altitude changes, number and frequency of stops,etc.) and/or for a particular driver. The available routes may also havecorrelated characteristics (amount of highway versus city driving,number and frequency of stops, etc.). The action module may access thefuel efficiency model and the correlated characteristics for the routesin order to assign a particular vehicle to a particular route, assign aparticular driver to driver a particular vehicle, and/or assign aparticular driver to drive a particular route using a particularvehicle. The driver and/or route optimization process may be incommunication with the enterprise scheduling systems to check foravailability and automatically assign drivers and vehicles to theappropriate routes.

The action module 227 may further provide driver incentives. The driverincentive process may encourage drivers to follow the guidelines forefficient driving behavior determined in the assess module 225 byproviding incentives for them in form of recognition, monetary rewardsand/or other kind. Reports may be generated in the alert module 226,allow management to see a list of best and worst drivers. The ratingsmay be objective because of the calculations and the baselineestablished in the assess module 225, thus allowing the management teamto provide fair and justified rewards to high performers and encouragingothers to improve their driving behavior. Therefore, this approachallows for an objective way to rate driving behavior, unlike previousmethod to rate driving behavior or determine any reasonable link betweenequipment failures or efficiency degradation and driving patterns of agiven driver.

While this invention has been shown and described in connection with thepreferred embodiments, it is apparent that certain changes andmodifications in addition to those mentioned above may be made from thebasic features of this invention. In addition, there are many differenttypes of computer software and hardware that may be utilized inpracticing the invention, and the invention is not limited to theexamples described above. The invention was described with reference toacts and symbolic representations of operations that are performed byone or more electronic devices. As such, it will be understood that suchacts and operations include the manipulation by the processing unit ofthe electronic device of electrical signals representing data in astructured form. This manipulation transforms the data or maintains itat locations in the memory system of the electronic device, whichreconfigures or otherwise alters the operation of the electronic devicein a manner well understood by those skilled in the art. The datastructures where data is maintained are physical locations of the memorythat have particular properties defined by the format of the data. Whilethe invention is described in the foregoing context, it is not meant tobe limiting, as those of skill in the art will appreciate that the actsand operations described may also be implemented in hardware.Accordingly, it is the intention of the Applicants to protect allvariations and modification within the valid scope of the presentinvention. It is intended that the invention be defined by the followingclaims, including all equivalents.

The flow charts in FIGS. 3-5 may be encoded in a signal bearing medium,a computer readable medium such as a memory, programmed within a devicesuch as on one or more integrated circuits, or processed by a controlleror a computer. If the methods are performed by software, the softwaremay reside in a memory resident to or interfaced to the multi-targetsystem 100, a communication interface, or any other type of non-volatileor volatile memory. The memory may include an ordered listing ofexecutable instructions for implementing logical functions. A logicalfunction may be implemented through digital circuitry, through sourcecode, through analog circuitry, or through an analog source such throughan analog electrical, audio, or video signal. The software may beembodied in any computer-readable or signal-bearing medium, for use by,or in connection with an instruction executable system, apparatus, ordevice. Such a system may include a computer-based system, aprocessor-containing system, or another system that may selectivelyfetch instructions from an instruction executable system, apparatus, ordevice that may also execute instructions.

A “computer-readable medium,” “machine-readable medium,”“propagated-signal” medium, and/or “signal-bearing medium” may compriseany means that contains, stores, communicates, propagates, or transportssoftware for use by or in connection with an instruction executablesystem, apparatus, or device. The machine-readable medium mayselectively be, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. A non-exhaustive list of examples of amachine-readable medium would include: an electrical connection“electronic” having one or more wires, a portable magnetic or opticaldisk, a volatile memory such as a Random Access Memory “RAM”(electronic), a Read-Only Memory “ROM” (electronic), an ErasableProgrammable Read-Only Memory (EPROM or Flash memory) (electronic), oran optical fiber (optical). A machine-readable medium may also include atangible medium upon which software is printed, as the software may beelectronically stored as an image or in another format (e.g., through anoptical scan), then compiled, and/or interpreted or otherwise processed.The processed medium may then be stored in a computer and/or machinememory.

While various embodiments of the invention have been described, it willbe apparent to those of ordinary skill in the art that many moreembodiments and implementations are possible within the scope of theinvention. Accordingly, the invention is not to be restricted except inlight of the attached claims and their equivalents.

1. An energy efficiency determination system comprising: a receiver thatreceives operational data about an asset from a sensor indicative of atleast one operational characteristic; a memory that stores theoperational data; an asset efficiency model indicative of energyefficiency of the asset; a processor in communication with the assetefficiency model and the memory, the processor analyzing real-timeenergy efficiency of the asset based on the operational data and theasset efficiency model, assessing whether to perform maintenance on theasset based at least on improved efficiency of the asset due to theperformed maintenance, and scheduling the maintenance based on theassessing whether to perform the maintenance, wherein the assetcomprises a vehicle; wherein the asset efficiency model comprises dataindicative of optimal efficiency of the vehicle, further whereinassessing whether to perform maintenance on the asset based at least onimproved efficiency of the asset due to the performed maintenancecomprises: determining a first cost of an increased fuel consumptionuntil a next scheduled vehicle maintenance event; determining a secondcost of performing maintenance on the asset; and comparing the firstcost impact with the second cost impact.
 2. The energy efficiencydetermination system of claim 1, wherein analyzing real-time energyefficiency of the asset based on the operational data and the assetefficiency model comprises analyzing fuel efficiency variance.
 3. Theenergy efficiency determination system of claim 2, wherein the assetefficiency model comprises a baseline measure of fuel economy; whereinthe operational data comprises average fuel economy; and whereinanalyzing fuel efficiency variance is based on the baseline measure offuel economy and the average fuel economy.
 4. The energy efficiencydetermination system of claim 1, wherein determining the second costcomprises determining a cost of removing the vehicle from service, alabor cost of performing maintenance on the vehicle outside an existingmaintenance schedule, a cost of parts for the maintenance, and a cost ofuse of a spare vehicle to replace the vehicle that is receivingmaintenance.
 5. The energy efficiency determination system of claim 1,further comprising an operational model for the vehicle to predictpotential failures; and wherein the processor predicts failure of thevehicle based on the operational data and the operational model andassesses whether to perform maintenance on the vehicle based on thepredicted failure.
 6. The energy efficiency determination system ofclaim 5, wherein assessing whether to perform maintenance on the vehiclebased on the predicted failure comprises: determining an urgency ofperforming maintenance; determining a cost allowing the predictedfailure to occur; determining a cost of performing maintenance; andcomparing the cost of allowing the predicted failure to occur versus thecost of performing maintenance.
 7. The energy efficiency determinationsystem of claim 6, wherein the processor determines who to alert basedon at least one of the urgency of performing maintenance, the costallowing the predicted failure to occur, or the cost of performingmaintenance.
 8. A method for determining energy efficiency, the methodcomprising: receiving operational data about an asset from a sensorindicative of at least one operational characteristic; storing theoperational data; analyzing real-time energy efficiency of the assetbased on the operational data and an asset efficiency model indicativeof energy efficiency of the asset; assessing whether to performmaintenance on the asset based at least on improved efficiency of theasset due to the performed maintenance; scheduling the maintenance basedon the assessing whether to perform the maintenance, wherein the assetcomprises a vehicle; and wherein the asset efficiency model comprisesdata indicative of optimal efficiency of the vehicle, further whereinassessing whether to perform maintenance on the asset based at least onimproved efficiency of the asset due to the performed maintenancecomprises: determining a first cost of an increased fuel consumptionuntil a next scheduled vehicle maintenance event; determining a secondcost of performing maintenance on the asset; and comparing the firstcost impact with the second cost impact.
 9. The method of claim 8,wherein analyzing real-time energy efficiency of the asset based on theoperational data and the asset efficiency model comprises analyzing fuelefficiency variance.
 10. The method of claim 9, wherein the assetefficiency model comprises a baseline measure of fuel economy; whereinthe operational data comprises average fuel economy; and whereinanalyzing fuel efficiency variance is based on the baseline measure offuel economy and the average fuel economy.
 11. The method of claim 8,wherein determining the second cost comprises determining a cost ofremoving the vehicle from service, a labor cost of performingmaintenance on the vehicle outside an existing maintenance schedule, acost of parts for the maintenance, and a cost of use of a spare vehicleto replace the vehicle that is receiving maintenance.
 12. The method ofclaim 8, further comprising: accessing an operational model for thevehicle to predict potential failures; predicting failure of the vehiclebased on the operational data and the operational model; and assessingwhether to perform maintenance on the vehicle based on the predictedfailure.
 13. The method of claim 12, wherein assessing whether toperform maintenance on the vehicle based on the predicted failurecomprises determining an urgency of performing maintenance, determininga cost allowing the predicted failure to occur, determining a cost ofperforming maintenance, and comparing the cost of allowing the predictedfailure to occur versus the cost of performing maintenance.
 14. Themethod of claim 13, further comprising determining who to alert based onat least one of the urgency of performing maintenance, the cost allowingthe predicted failure to occur, or the cost of performing maintenance.